David Andrew Barry

Contribution of anaerobic microbial activity to natural attenuation of benzene in groundwater

Anaerobic biodegradation of hydrocarbons, using a variety of terminal electron acceptors (TEAs), is increasingly being reported both in laboratory studies and in the field. Of all the petroleum hydrocarbons, benzene is considered the most problematical due to its high toxicity and relatively high aqueous solubility. These, combined with its peculiarly stable structure, mean that it has long been considered recalcitrant in all but aerobic conditions. There is now a small, but growing, literature to suggest that this may not in fact be the case. We present an assessment of the field, encompassing reviews up to 1997 and original papers published since then. It appears that benzene is indeed degraded anaerobically, but that organisms capable of doing so are not ubiquitous. In addition, benzene degradation may be competitively inhibited by the presence of more readily degraded compounds such as toluene. Certainly, the occurrence and rate of benzene attenuation under anaerobic conditions is far more site-specific than for other benzene, toluene, ethylbenzene and xylenes (BTEX) compounds. We discuss a mathematical method for modelling redox-dependent, differential degradation rates.

Prediction of Longshore Sediment Transport Using Soft Computing Techniques

An important task for coastal engineers is to predict the sediment transport rates in coastal regions with correct estimation of this transport rate, it is possible to predict both natural morphological or beach morphology changes and the influence of coastal structures on the coast line. A large number of empirical formulas have been proposed for predicting the longshore sediment transport rate as a function of breaking wave characteristics and beach slope. The main shortcoming of these empirical formulas is that these formulas are not able to predict the field transport rate accurately. In this paper, an Adaptive- Network-Based Fuzzy Inference System which can serve as a basis for consulting a set of fuzzy IF-THEN rules with appropriate membership functions to generate the stipulated input-output pairs, is used to predict and model longshore sediment transport. For statistical comparison of predicted and observed sediment transport, bias, Root Mean Square Error, and scatter index are used. The results suggest that the ANFIS method is superior to empirical formulas in the modeling and forecasting of sediment transport. We conclude that the constructed models, through subtractive fuzzy clustering, can efficiently deal with complex input–output patterns. They can learn and build up a neuro-fuzzy inference system for prediction, while the forecasting results provide a useful guidance or reference for predicting longshore sediment transport.